Polar Sine Based Siamese Neural Network for Gesture Recognition

Abstract : Our work focuses on metric learning between gesture sample signatures using Siamese Neural Networks (SNN), which aims at model-ing semantic relations between classes to extract discriminative features. Our contribution is the notion of polar sine which enables a redefini-tion of the angular problem. Our final proposal improves inertial gesture classification in two challenging test scenarios, with respective average classification rates of 0.934 ± 0.011 and 0.776 ± 0.025.
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Samuel Berlemont, Grégoire Lefebvre, Stefan Duffner, Christophe Garcia. Polar Sine Based Siamese Neural Network for Gesture Recognition. International Conference on Artificial Neural Networks, Sep 2016, Barcelona, Spain. ⟨10.1007/978-3-319-44781-0_48⟩. ⟨hal-01369302⟩

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